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   "id": "392bd175",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stock imports\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from lee_et_al_2023.src import analysis\n",
    "from lee_et_al_2023.src import base\n",
    "from lee_et_al_2023.src import data_loaders\n",
    "base.set_visual_settings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18c593dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rick/code/ratatouille/qian_et_al_2023/src/data_loaders.py:77: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  panel = humans.groupby('RedJade Code').mean().loc[mol_codes, base.MONELL_CLASS_LIST]\n"
     ]
    }
   ],
   "source": [
    "models, humans, panel, subjects = data_loaders.get_clean()\n",
    "extended_models = data_loaders.load_extended_predictions(panel.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4057a825",
   "metadata": {
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   "source": [
    "corrs = analysis.fast_process(humans, extended_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "904aa230",
   "metadata": {},
   "outputs": [],
   "source": [
    "corrs.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed15000c",
   "metadata": {},
   "source": [
    "(The rest of the plotting code for this subfigure is done in R.)"
   ]
  }
 ],
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